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He was extremely skeptical of the capabilities of AI for years but eventually came around to calling it truly capable after testing advanced LLMs. He is still
by ilaksh 8d ago
He was extremely skeptical of the capabilities of AI for years but eventually came around to calling it truly capable after testing advanced LLMs.
He is still an AI skeptic in as many ways that he can reasonably be, but doesn't deny the raw ability. Actually he thinks it will eclipse humans and he is a doomer.
He sees it as being an extremely empty type of intelligence though.
But I hope that people who have an intuitive understanding of contemporary machine learning (not me) will sometimes watch videos like this and think about things at a higher level. LLMs have a LOT of assumptions built in.
- gaigalas 8d agosources?
- morkalork 8d agoI appreciate that he did not fall into the anthropomorphization trap unlike Noam Chomsky
- calebkaiser 8d agoI'd wager that the vast majority of the ML research community, especially anyone interested in "AGI", is familiar with Hofstader's work. And I don't think anyone working on contemporary language models would argue that they are somehow an assumption-less "pure" model--the particular inductive bias of the Transformer has been studied by a huge number of researchers and continues to be, and the same is true for things like training data bias. I think the Hofstader's view of modern LLMs is actually a deeply human and touching one. Looking at his work over the years, his curiosity has always veered towards human thought. He could have written GEB with a focus on completely different examples of self-reference, but he chose three striking humans from history. When he's describing modern systems as "empty intelligence", I think there's a little bit of heartbreak in his perspective, because he sees them as fundamentally different from humans in a way that leaves the part he loves--the "I" in the loop--out of the equation. He gave an interview a few years ago where he explains his feeling as being "diminished" not in a "What will I do if I'm not the best at math?" kind of way, but more specifically as he puts it, that humans are "imperfect, flawed structures".
- dnautics 8d agoThere is no reason to believe that the transformers couldn't be doing something close to what copycat does (especially with thinking tokens), as an emergent phenomenon of the sheer size of the corpus. The architecture is certainly capable of encoding the actions in copycat anyways.
- antonvs 7d ago> He sees it as being an extremely empty type of intelligence though. My instinctive reaction to claims like this is, is it really any different from humans? Why? Current LLMs have a lot of limitations. The pretraining without the ability to learn beyond their context is a big one. Every prompt is literally instantiating a new instance of the intelligence. If you take these limitations into account, it's not at all clear to me that they're all that different from humans when it comes to the fundamentals: we learn from training data, we respond according to what we've learned. In fact, many humans resemble the pretrained model quite closely - they do most of their learning in their early years, and don't update much after that except in relatively limited ways. You do get lifelong learners, but they're the exception. Taking all this into account, why is the one kind of intelligence "extremely empty" whereas the other is not? Is this not just anti-machine bias when it comes to intelligence?